An adaptive and lightweight YOLOv5 detection model for lung tumor in PET/CT images.

ABSTRACT: Multi-modal medical images are important in tumor lesion detection. However, the existing detection models only use single-modal to detect lesions, a multi-modal semantic correlation is not enough to consider and lacks ability to express the shape, size, and contrast degree features of lesions. A Cross Modal YOLOv5 model (CMYOLOv5) is proposed. Firstly, there are two networks, auxiliary network is consisted by dual-branch structure to extract semantic information from PET and CT, backbone network is consisted by YOLOv5 to extract semantic information from PET/CT. Secondly, Cross-modal Features Fusion (CFF) is designed in auxiliary network to fuse PET functional information and CT anatomical information. Self-Adaptive Attention Fusion (AAF) is designed in backbone network to fuse and enhanc

SUBMITTER: Zhou T 

PROVIDER: S-EPMC11681088 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature